阎爱侠 - 北京化工大学 - 生命科学与技术学院

个人简介

教育背景： 1.1991,9-1995,6，兰州大学化学系，获学士学位 2.1995,9-2000,6，兰州大学化学系，获理学博士学位工作经历： 1.2001,1-2003,1，德国爱尔兰根大学，作为洪堡研究员进行研究工作 2.2003,2-2005,1，英国牛津大学化学系，作为研究科学家进行研究工作 3.2010,4-2010,6，德国波恩大学，作为洪堡研究员进行研究工作 4.2005,2-今，北京化工大学，在生命科学与技术学院制药工程系工作

研究领域

主要研究领域为计算机辅助药物设计，以重要生物靶标的抑制剂及小分子药物的生物活性为研究对象，充分利用生物信息学、化学信息学、计算化学等工具和方法，进行药物信息及数据挖掘、多种机器学习方法的建模、化合物虚拟筛选等研究，以预测新化合物的生物活性，寻找和设计新的先导化合物。

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

(1) Qin, Z.J.; Xi, Y.; Zhang, S.D.; Tu, G.P.; Yan, A.X.* Classification of Cyclooxygenase-2 Inhibitors using Support Vector Machine and Random Forest Methods, Journal of Chemical Information and Modeling, 2019, 59 (5) , 1988-2008. (2) Zhang, S.D.; Li, Y.; Qin, Z.J.; Tu, G.P.; Chen, G.; Yan, A.X.*SAR study on inhibitors of GIIA secreted phospholipase A2 using machine learning methods, Chemical Biology & Drug Design, 2019, 93 (5), 666–684. (3) Kong, Y.; Bender, A.; Yan, A.X.*Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening. Journal of Chemical Information and Modeling, 2018, 58(1), 36-47. (4) Li, Y.; Tian, Y.J.; Qin, Z.J.; Yan, A.X.* Classification of HIV‑1 Protease Inhibitors by Machine Learning Methods, ACS Omega, 2018, 3,15837-15849. (5) Zhang, M.D.; Xia, Z.H.; Yan, A.X.*; Computer modeling in predicting the bioactivity of human5-lipoxygenase inhibitors, Molecular Diversity, 2017, 21, 235–246. (6) Wang, M.L.; Li, L; Yu, C.Y.; Yan, A.X.*; Zhao, Z. Z.; Zhang, G.; Jiang, M.; Lv, A.P.; Gasteiger, J. Classification of Mixtures of Chinese Herbal Medicines Based on a Self-Organizing Map (SOM), Molecular Informatics, 2016, 35(3-4), 109-115. (7) Li, Y.; Xuan, S.Y.; Feng, Y.;Yan, A.X.*Targeting HIV-1 integrase with strand transfer inhibitors, Drug Discovery Today,2015, 20(4), 435-449. (8) Wang, M.L.; Zhong, M.; Yan, A.X.*; Li, L.; Yu, C.Y. Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors, SAR and QSAR in Environmental Research, 2014, 25(1), 1-15. (9) Zhong, M.; Nie, X.L.; Yan, A.X.*;Yuan, Q.P. Carcinogenicity Prediction of Noncongeneric Chemicals by a Support Vector Machine, Chemical Research in Toxicology, 2013, 26(5), 741-749. (10) Heikamp,K.; Hu, X.Y.; Yan, A.X.;Bajorath, J. Prediction of Activity Cliffs Using Support Vector Machines, Journal of Chemical Information and Modeling, 2012, 52 (9), 2354-2365. (11) Yan, A.X.*; Wang, L.; Xu, S.Y.; Xu J. Aurora-A Kinase Inhibitor Scaffolds and Binding Modes, Drug Discovery Today, 2011, 16(5-6),260-269. (12) Wang, Z.; Chen, Y.Y.; Liang, H.; Bender, A.; Glen, R.C.; Yan, A.X.* P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set, Journal of Chemical Information and Modeling, 2011, 51(6), 1447-1456. (13) Yan, A.X.; Grant, G. H.; Richards, W. G. Dynamics of Conserved Waters in Human HSP 90: Implications for Drug Design, Journal of the Royal Society Interface, 2008. 5(supp3), S199–S205. (14) Yan, A.X.* Application of Self-Organizing Maps in Compounds Pattern Recognition and Combinatorial Library Design, Combinatorial Chemistry & High Throughput Screening, 2006, 9(6), 473-480. (15) Yan, A.X. andGasteiger, J., Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation, Journal of Chemical Information and Computer Science, 2003, 43(2), 429-434. (16) Yan, A.X.* Prediction of ADME Properties, in “Applied Chemoinformatics: Achievements and Future Opportunities”, pp333-358, Editors: T. Engel, J. Gasteiger, Wiley-VCH, Weinheim, 2018. (外文专著)